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https://www.analyticsvidhya.com/blog/2015/08/common-machine-learning-algorithms/ https://www.datascience.com/blog/introduction-to-forecasting-with-arima-in-r-learn-data-science-tutorials https://techietweak.wordpress.com/2016/05/18/r-data-frame-the-one-data-type-for-predictive-analytics/ Linear Regression Model: TestDataset <- read.csv("D:\\Dinesh\\AI\\TestDataset.csv") TrainingDataset <- read.csv("D:\\Dinesh\\AI\\TrainingDataset.csv") TrainingDataset TestDataset date <- as.Date(TrainingDataset$dteday) TrainingDataset <- cbind(TrainingDataset,date) sapply(TrainingDataset, class) date <- as.Date(TestDataset$dteday) TestDataset <- cbind(TestDataset,date) sapply(TestDataset, class) lmModel <- lm(cnt ~ date + season + yr + mnth + holiday + weekday + workingday + weathersit + temp + atemp + hum + windspeed + casual + registered, data = TrainingDataset) glmModel <- glm(cnt ~ date + season + yr + mnth + holiday + weekday + work...

DAX for filtering data up to given Year in Power BI Report: Create MaxDateKey measure in FactFinancial table using below DAX MaxDateKey = CALCULATE(MAX('Date'[DateKey]), ALLEXCEPT('Date', 'Date'[FYYear])) Revenue = CALCULATE(sum(FactFinancial[Amount] ),DimAccount[AccountType] = "Income", FILTER(DimDate, DimDate[DateKey] <= [MaxDateKey] )) All other measure like QoQ and Total Amount you apply below logic Revenue = CALCULATE(sum(FactFinancial[Amount] ),DimAccount[AccountType] = "Income", FILTER(DimDate, DimDate[FYYear] = max('Date'[FYYear]) )

Find installed R Packages in SQL/R Process sp_execute_external_script @language = N'R', @script = N'OutputDataSet <- data.frame(installed.packages())' Install Additional R Packages https://msdn.microsoft.com/en-in/library/mt591989.aspx  Revolution R Enterprise inside SQL Server EXECUTE   sp_execute_external_script @language = N'R' ,@script = N' salesData <- InputDataSet fit <- lm(SalesAmount ~ Year + Quarter, data = as.data.frame(salesData)) data2017 <- data.frame(Year=2017, Quarter=1:4) sales2017 <- predict(fit, newdata=data2017) style <- c(rep(1,14), rep(2,4)) salesForecast <- data.frame(data2017, sales2017) library(sqldf) OutputDataSet <- sqldf("select Quarter, Year, SalesAmount from salesData UNION select Quarter, Year, sales2017 from salesForecast") ' ,@parallel = 1 ,@input_data_1 = N'select d.CalendarQuarter Quarter, CalendarYear Year, sum(f.SalesAmount) SalesAm...

BI Concept:  Star Schema: The star schema has a center, represented by a fact table, and the points of the star, represented by the dimension tables. Snowflake Schema: In a snowflake, the dimension tables are normalized. ODS: ODS should be considered only when information retrieval requirements are to be real time and cannot access OLTP systems because of criticality of resources. Dimension Types: Changing Dimension:  Changing dimension is the dimension which has at least one attribute whose value would change over the time. Slowly Changing Dimensions: Attributes of a dimension that would undergo changes over time. For example: Employee work location  • Type I: The existing record is updated with change. • Type II: History is preserved, a new record is added to dimension and old record is marked as inactive.  EndDate or IsLatest is generally used to represent latest record of a business key. • Type III: Type III preserve...

Dimensions and Measure Groups can be related in different ways Regular - A Direct relationship.  Key column for the dimension is joined directly to the fact table Fact - A Column in the fact table can be treated as a  degenerate dimensions  (usually used for drillthrough).  Useful dimensional data is sometimes stored in a fact table to reduce duplication. Referenced - A Dimension joined through another dimension is treated as if directly joined to the fact table.  Key column for the dimension is joined indirectly to the fact table through a key in another dimension table Many-to-Many - Handles many-to-many fact relationships. I t is frequently useful to join a single fact to multiple dimension members.

Usage-Based Optimization is a fantastic feature in SSAS to improve the most frequently used MDX queries of any cube database. This feature is used to design aggregations based on actual user queries using Business Intelligence Development Studio. But when ever you try to design  Usage-Based Optimization  you might have seen all the options  disabled.  This uses the technique of creating aggregations on FREQUENTLY USED queries in the given time interval.  We can enable the options in Usage-Based Optimization wizard. Connect to SSAS using SSMS (Sql Server Management Studio). Right click on the server and select properties as shown below. set CreateQueryLogTable to  true. Set the value of  QueryLogConnectionString  to any database where you wish to store your queries. Set the value of  QueryLogSampling  to the appropriate value for your environment (the default value of 10 means every 10th query will be logged). The lowe...